人工智能平台 CASMI_AI_V2.0.19

Radiomics 1.2.19.0328

CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer

Key words: Gastric cancer; Multi-detector computed tomography; Radiomics; Deep learning

 

Abstract

To develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer, a total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. The contrast enhanced CT images of three phases were manually segmented. Conventional hand-crafted features and deep learning features were extracted based on CT images automatically and were utilized to build radiomics signatures via machine learning methods. Multivariable logistic regression analysis was used to develop a diagnostic model (radiomics nomogram) incorporating the radiomics signatures and subjective CT findings. In the experiments, the nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95% CI, 0.86-0.94), 0.87 (95% CI, 0.82-0.92) and 0.90 (95% CI, 0.85-0.96) respectively.


      人工智能平台(以下简称AI平台)是一款肿瘤分析预测软件,主要针对肿瘤的影像信息,通过医学影像处理技术对影像进行病灶分割及特征提取,同时采用以医学大数据为基础的人工智能等手段对肿瘤进智能识别及精准预测。

      AI平台旨在协助医生对肿瘤影像进行病情的分析,对个体病人予以科学规范的预后建议,从而提高临床诊断效率。目前AI平台搭载了五个AI算法:肺结节良恶性分析、肺炎检测、甲状腺良恶性分析、肝纤维化分析和肝微血管侵犯分析。


 


影像组学辅助诊断软件-Radiomics 1.2.19.0328                         

1) 解决了不能读取压缩格式Dicom文件的Bug;

2) 解决了选取种子点容易崩溃的Bug。